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Algorithm Engineer Graduate (Global Live - Strategy) - 2026 Start (PhD)

TikTok

Skills

Required

  • machine learning
  • C/C++ / Python
  • Linux
  • multi-threaded / multi-process
  • deep learning algorithms
  • reinforcement learning
  • probability
  • numerical optimization
  • to data
  • Flink
  • Spark
  • dnn

Preferred

  • recommend/advertising systems / risk control systems / LLM
  • LIVE
  • Build prediction/uplift NN model
  • improve LIVE feature intelligence
  • ByteDance

About TikTok

We are looking for talented individuals to join our team in 2026. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth. Launch your career where inspiration is infinite at our Company.

Responsibilities

  • The strategy team's mission is to use model-driven and data-driven ways to promote core indicators on LIVE revenue and user engagement, which includes:
  • Build prediction/uplift NN model to improve LIVE feature intelligence;
  • Mine and interfere potential crowds to gain a higher ROI operation for both LIVE streamer and watcher;
  • Make detailed analysis on user data and system data to find out user experience optimiaztion zoom;
  • Other work like user growth strategy, cold-start strategy, etc.
  • Participate in the optimization of ByteDance LIVE monetization business and serve the LIVE experience of billions of global users;
  • Adopt cutting-edge machine learning model to scientifically model massive user behaviour and consumption data, build high-performance/high-reliability architecture services, and bring users the ultimate experience in LIVE consumption;
  • Deeply participate in product/business discussions, fully analyze and understand user behaviour patterns, and provide optimization directions for the overall business.

Requirements

  • Successful candidates must be able to commit to an onboarding date by end of year 2026.
  • Please state your availability and graduation date clearly in your resume.
  • Currently pursuing a PhD in Computer Science, engineering quantitative field
  • Familiar with C/C++/ Python development, familiar with Linux multi-threaded /multi-process;
  • Familiar with commonly used machine learning and deep learning algorithms, including but not limited to boost machine, dnn, reinforcement learning, etc.;
  • Solid maths foundation, including probability statistics, numerical optimization, etc., sensitive to data, good at discovering, analyzing and solving problems from data;
  • Familiar with at least one of Flink, Spark, Hive etc., with big data parallel processing experience;

Preferred

  • Experience in large-scale recommend/advertising systems, risk control systems or LLM is preferred

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